DACHSER establishes a competence center for data science and machine learning

AI technologies and methods have already proven their performance and benefits in various projects and applications at DACHSER. “The importance of artificial intelligence, machine learning, and data science for transportation, logistics, and supply chain management will continue to grow in the coming years. That makes it crucial for DACHSER to further strengthen its expertise in this important field and to further expand its ability to implement and operate machine learning applications,” says Stefan Hohm, Chief Development Officer (CDO). CC DS&ML will take on this task at DACHSER and act as a central point of contact.

DACHSER produces large volumes of data on a daily basis, and this forms a foundation for the development and use of the new AI technologies. “We will make even better use of this data in the future: it will help us find and implement new solutions for a wide variety of use cases,” says Florian Zizler, Team Leader Competence Center Data Science & Machine Learning.

Anticipating capacity fluctuations with artificial intelligence

One specific sample application for the work of the newly created competence center is an AI product that was developed and rolled out as part of the DACHSER Enterprise Lab. The forecasting model uses machine learning techniques to predict a branch’s inbound overland transport volumes up to 25 weeks in advance. “Our data goes back as far as 2011. The focus is on historical shipment data,” Zizler says. “We supplement this data pool with calendar data, such as public holidays or school vacations. This enables the model to recognize the seasonal patterns that are so important in overland transport. To better anticipate trends, we’ve also integrated a wide variety of economic indices.” As a result, DACHSER can provide employees in its branches with valuable support on decisions relating to seasonal capacity planning. It is precisely in this area that it is important to obtain appropriate load capacity on the market at an early stage, or to plan resources in the transit terminal. However, the current conditions also have an impact here. “Of course, it was a challenge for forecasts based on past values to cope with volatile volume fluctuations as well as the coronavirus pandemic,” Zizler says. But he and his team of experts remain optimistic: “We’ll soon get our forecasting back to its usual high quality.”